fusionquery's Introduction

FusionQuery

Python implementation of FusionQuery in paper FusionQuery: On-demand Fusion Queries over Multi-source Heterogeneous Data.

Dependencies

Python 3.8
sentence-transformers 2.2.2
faiss-gpu 1.7.2
numpy 1.23.1
pytorch 1.12.1

Datasets

This repo contains two datasets, Movie and Book. We released KG version of these two datasets in the data. Each data source is stored in three files. Entities in source n, are stored in ent_ids_n, relations are stored in rel_ids_n and triples are stored in triples_n. The queries conducted on the datasets are stored in query.json.

More datasets can be found in this web

Run code

Perform the entire workflow of FusionQuery.

python main.py --data_root "./data/movie" \
--data_name movie \
--fusion_model FusionQuery \
--types JSON KG CSV \
--iters 20 \
--thres_for_query 0.9 \
--thres_for_fusion 0.4

The more detailed information about arguments is listed as follows.

Arguments	Explainations	Default
`--data_root`	root path of data	`../data/movie`
`--data_name`	data name used in the current experiment	`movie`
`--fusion_model`	data fusion methods used in the framework (e.g., FusionQuery, DART, CASE, etc.)	`FusionQuery`
`--types`	data types used in the current experiment (a list)	`JSON KG CSV`
`--iters`	maximum iterations for convergence	`20`
`--thres_for_query`	initial matching threshold $\tau$	`0`
`--thres_for_fusion`	threshold for data veracity	`0.5`
`--gpu`	the gpu device id	`0`
`--seed`	random seed	`2021`

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